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Google interview notes, page 56 of 215

FAANG / Big Tech

Most of these Google write-ups come from Software Engineers (1,167), then Product Managers (686) and Data Scientists (125). The most reported rounds are the product sense round (667), the technical phone screen (660), the behavioral / leadership round (252) and the onsite coding round (214).

2146 interview notes · updated Oct 2026

Google·Product Manager·Onsite - Product Sense / Strategy

May 2026
Google PM interview with one product question about Maps. Not a lot of context to go on, but it was the kind of open-ended experimentation question that can go sideways fast if you don't anchor it early.
  • How would you design A/B tests to identify and reduce user frustration in Google Maps?

“My first instinct was to jump straight into test ideas, which was a mistake.”

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Google·Product Manager·Onsite - Behavioral / Leadership

May 2026
PM interview at Google with a behavioral question that sounds straightforward until you realize they actually want you to sit with being wrong, not just narrate a tidy recovery arc.
  • Describe a time you made a decision based on data and the outcome proved you wrong.

“I went in thinking I had a good story but halfway through I realized I was unconsciously framing it so I still looked smart.”

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Google·Product Manager·Onsite - Product Sense / Strategy

May 2026
Got a strategy question at Google that basically asked me to critique the company I was interviewing at. No pressure.
  • What technology trends is Google currently missing out on, and what should the company do about them?

“This one threw me a bit because you're essentially sitting in Google's house telling them what they're getting wrong.”

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Google·Product Manager·Onsite - Behavioral / Leadership

May 2026
Went through a behavioral round at Google for a PM role. Just the one question about stakeholders, which sounds easy until you're actually sitting there trying to pick the right story.
  • Tell me about a time you had to manage a difficult stakeholder.

“I picked a story about a cross-functional partner who kept pushing back on roadmap decisions.”

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Google·Software Engineer·Onsite - Behavioral / Leadership

May 2026
Standard behavioral round for a Software Engineer role at Google. Nothing too surprising, just the usual questions about collaboration and past projects. Felt pretty formulaic but still needed real answers.
  • What's the most interesting project you've worked on?
  • Tell me about a time you worked effectively with someone who had a very different working style or personality than yours.
  • Describe a situation where you learned something valuable from someone with a different background than yours.
  • Can you share a time you helped a colleague or team even though you had more urgent priorities of your own?
  • How do you approach challenges when things aren't going the way you expected?

“This one sounds easy until you're sitting there realizing all your 'interesting' projects sound boring when you say them out loud.”

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Google·Product Manager·Onsite - Product Sense / Strategy

May 2026
Google PM interview, one question about growing Gmail ads revenue by a specific percentage. Pretty focused case, no fluff.
  • How would you grow Gmail ads revenue by 20%?

“I went straight to user segments and ad placement levers, which felt right but I probably skipped over defining what '20%' actually means in context.”

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Google·Product Manager·Onsite - Product Sense / Strategy

May 2026
Google PM interview with a classic product design question about amusement parks. Not much context to go on, but these open-ended experience improvement questions are pretty standard for Google product rounds.
  • How would you improve the experience at amusement parks?

“Classic Google-style product sense question where the scope is deliberately huge.”

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Google·Software Engineer·Recruiter / HR Screen

May 2026
Behavioral screen for a software engineer role at Google. Just one career goals question, pretty standard stuff.
  • What are your short-term and long-term career goals, why do they matter to you, and how does this role fit into that picture?

“I fumbled a bit because I hadn't thought about the 'why meaningful to you' part, I just had a rehearsed answer about growth and impact that felt hollow even as I said it.”

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Google·Machine Learning Engineer·Technical Phone Screen

May 2026
ML Engineer loop at Google, pretty heavy on fundamentals across the board. Five questions back to back covering everything from classic algorithms to transformer internals. Felt like a breadth test more than a depth one, which I wasn't fully expecting.
  • Walk through the core principles and assumptions behind logistic regression.
  • How does Naive Bayes work, and under what conditions does it tend to perform well?
  • Describe the transformer architecture and explain how self-attention contributes to its effectiveness.
  • What metrics would you use to evaluate a multi-class classification model, and why those specifically?
  • Compare bagging and boosting as ensemble methods. How does each one reduce model error?

“I knew this one cold so I led with the linear decision boundary assumption and the sigmoid output, then hit the independence of features and the log-odds interpretation.”

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Google·Machine Learning Engineer·Technical Phone Screen

May 2026
Google ML engineer interview with a coding problem around string validation. Pretty straightforward premise but the edge cases are where things get interesting.
  • Given a string, determine whether the parentheses in it are correctly placed and balanced.

“Classic stack problem but I second-guessed myself on the edge cases, like empty strings and nested mixed brackets.”

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